1057. The Impact of Temporary Suspensions of an Antimicrobial Stewardship Audit and Feedback Program on Antimicrobial Utilization of General Internal Medicine Inpatients
Bibliographic record
Abstract
Abstract Background A goal of Antimicrobial Stewardship Programs (ASP) is to optimize antimicrobial use; many using audit and feedback (AAF). Although AAF decreases unnecessary target antimicrobial use, it is resource-intensive. As a result, temporary suspensions in AAF activity may occur from human resource limitations or other factors. We describe the impact of these temporary suspensions and intensity of care on antimicrobial utilization trends. Methods This retrospective study describes the initiation and temporary suspensions of AAF in the General Internal Medicine (GIM) unit at an urban teaching hospital. Data were collected over 65 months. During active-AAF, a dedicated ID trained clinical pharmacist and ID physician-reviewed antimicrobial use for all GIM patients and provided patient-specific advice to physicians. Antimicrobial use was measured by Defined Daily Doses (DDD) normalized per 1,000 patient-days. To assess the impact of temporary suspensions, data were compared in two ways: 1. All nonactive-AAF time-frames were compared with active AAF 2. Pre-ASP was compared with Post-ASP Initiation which includes suspension periods. To determine whether differences in trends were seen based on acuity level of the patients (identified at admission as benefiting from frequent monitoring), analyses were repeated after stratification of patients admitted to the Step-Up unit (GIM-SU) and the regular ward (GIM-W). Results Comparing nonactive AAF vs. active-AAF, significant changes (P < 0.05) in mean normalized DDD were observed for total antimicrobials (-19%), antipseudomonals (-21%) fluoroquinolones (−41%) and first-generation β lactams (−30%). Pre ASP vs. Post ASP comparisons showed similar but less pronounced trends. Following stratification to GIM-SU and GIM-W, greater variation in significant changes to targeted antimicrobials between comparisons was observed. Different significant antimicrobial changes were seen in SU vs. W. Conclusion Our results show that the temporary suspension of ASP AAF impacts antimicrobial utilization trends. Greater sustained decreases in targeted antimicrobials utilization were associated with active AAF. Stratification by patient acuity lead to increased variation in the impact on target antimicrobials and increased the impact of suspension. Disclosures All authors: No reported disclosures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".